Mariya Layurova
- Materials Chemistry top 10%
- Quantum Dots Synthesis And Properties 2
- Machine Learning in Materials Science 1
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- Perovskite Materials and Applications 4
- Chalcogenide Semiconductor Thin Films 4
- solar cell performance optimization 2
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- Energy, Environment, and Transportation Policies 1
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- Semiconductor Quantum Structures and Devices 1
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- Climate Change Policy and Economics 1
- Co-authors
- Shijing SunTonio BuonassisiNoor Titan Putri HartonoJuan‐Pablo Correa‐BaenaJanak ThapaZekun RenFelipe OviedoSiyu Tian
- Partner nations
- United StatesSingaporeGermany
In The Last Decade
Mariya Layurova
8 papers receiving 493 citations
Peers
Comparison fields: 5 of 50
- Materials Chemistry 379
- Electrical and Electronic Engineering 391
- Polymers and Plastics 71
- Renewable Energy, Sustainability and the Environment 47
- Electronic, Optical and Magnetic Materials 35
Countries citing papers authored by Mariya Layurova
This map shows the geographic impact of Mariya Layurova's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mariya Layurova with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mariya Layurova more than expected).
Fields of papers citing papers by Mariya Layurova
This network shows the impact of papers produced by Mariya Layurova. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mariya Layurova. The network helps show where Mariya Layurova may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Mariya Layurova, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 28 | |
| 2 | 2020 | 1 | |
| 3 | Author Correction: Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics (npj Computational Materials, (2020), 6, 1, (9), 10.1038/s41524-020-0277-x) | 2020 | 1 |
| 4 | 2019 | 33 | |
| 5 | 2019 | 2 | |
| 6 | 2019 | 250 | |
| 7 | 2019 | 15 | |
| 8 | 2018 | 167 |
About Mariya Layurova
Mariya Layurova is a scholar working on Electrical and Electronic Engineering, Materials Chemistry and Renewable Energy, Sustainability and the Environment, having authored 8 papers that have together received 497 indexed citations. Recurring topics across this work include Perovskite Materials and Applications (4 papers), Chalcogenide Semiconductor Thin Films (4 papers), Quantum Dots Synthesis And Properties (2 papers), solar cell performance optimization (2 papers), Semiconductor Quantum Structures and Devices (1 paper), Climate Change Policy and Economics (1 paper), Energy, Environment, and Transportation Policies (1 paper) and Machine Learning in Materials Science (1 paper). The work is most often cited by research in Materials Chemistry (379 citations), Electrical and Electronic Engineering (391 citations) and Polymers and Plastics (71 citations). Mariya Layurova has collaborated with scholars based in United States, Singapore and Germany. Frequent co-authors include Shijing Sun, Tonio Buonassisi, Noor Titan Putri Hartono, Juan‐Pablo Correa‐Baena, Janak Thapa, Zekun Ren, Felipe Oviedo, Siyu Tian, Ian Marius Peters and Sarah Wieghold. Their work appears in journals such as Chemistry of Materials, Applied Energy and Joule.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.